The *args
and **kwargs
is a common idiom to allow arbitrary number of arguments to functions as described in the section more on defining functions in the Python documentation.
The *args
will give you all function parameters as a tuple:
def foo(*args):
for a in args:
print(a)
foo(1)
# 1
foo(1,2,3)
# 1
# 2
# 3
The **kwargs
will give you all
keyword arguments except for those corresponding to a formal parameter as a dictionary.
def bar(**kwargs):
for a in kwargs:
print(a, kwargs[a])
bar(name='one', age=27)
# name one
# age 27
Both idioms can be mixed with normal arguments to allow a set of fixed and some variable arguments:
def foo(kind, *args, **kwargs):
pass
It is also possible to use this the other way around:
def foo(a, b, c):
print(a, b, c)
obj = {'b':10, 'c':'lee'}
foo(100,**obj)
# 100 10 lee
Another usage of the *l
idiom is to unpack argument lists when calling a function.
def foo(bar, lee):
print(bar, lee)
l = [1,2]
foo(*l)
# 1 2
In Python 3 it is possible to use *l
on the left side of an assignment (Extended Iterable Unpacking), though it gives a list instead of a tuple in this context:
first, *rest = [1,2,3,4]
first, *l, last = [1,2,3,4]
Also Python 3 adds new semantic (refer PEP 3102):
def func(arg1, arg2, arg3, *, kwarg1, kwarg2):
pass
Such function accepts only 3 positional arguments, and everything after *
can only be passed as keyword arguments.
Note:
- A Python
dict
, semantically used for keyword argument passing, are arbitrarily ordered. However, in Python 3.6, keyword arguments are guaranteed to remember insertion order.
- "The order of elements in
**kwargs
now corresponds to the order in which keyword arguments were passed to the function." - What’s New In Python 3.6
- In fact, all dicts in CPython 3.6 will remember insertion order as an implementation detail, this becomes standard in Python 3.7.
is
will return True
if two variables point to the same object, ==
if the objects referred to by the variables are equal.
>>> a = [1, 2, 3]
>>> b = a
>>> b is a
True
>>> b == a
True
# Make a new copy of list `a` via the slice operator,
# and assign it to variable `b`
>>> b = a[:]
>>> b is a
False
>>> b == a
True
In your case, the second test only works because Python caches small integer objects, which is an implementation detail. For larger integers, this does not work:
>>> 1000 is 10**3
False
>>> 1000 == 10**3
True
The same holds true for string literals:
>>> "a" is "a"
True
>>> "aa" is "a" * 2
True
>>> x = "a"
>>> "aa" is x * 2
False
>>> "aa" is intern(x*2)
True
Please see this question as well.
Best Answer
It's part of an evolution.
Originally, Python
items()
built a real list of tuples and returned that. That could potentially take a lot of extra memory.Then, generators were introduced to the language in general, and that method was reimplemented as an iterator-generator method named
iteritems()
. The original remains for backwards compatibility.One of Python 3’s changes is that
items()
now return views, and alist
is never fully built. Theiteritems()
method is also gone, sinceitems()
in Python 3 works likeviewitems()
in Python 2.7.